A model of humans playing the simple game of Paper Rock Scissors based on the ACT-R architecture (Anderson, 1993; Anderson & Lebiere, 1998) is presented. This model stores in long-term memory sequences of moves and attempts to anticipate the opponent's moves by retrieving from memory the most active sequence. This results in a tightly linked dynamical system in which each player drives the play of its opponent. The performance of this model as a function of the length of the sequences stored and the amount of noise in the
system is investigated, and is compared to the performance of human subjects.
